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‎content/blog/2020-08-26-fb-survey.html

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toc: true
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---
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<script src="/rmarkdown-libs/header-attrs/header-attrs.js"></script>
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<div id="TOC">
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<ul>
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<li><a href="#short-background">Short Background</a></li>
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<li><a href="#why-run-these-surveys">Why Run These Surveys?</a></li>
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<li><a href="#whats-in-the-survey">What’s in the Survey?</a></li>
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<li><a href="#some-interesting-examples">Some Interesting Examples</a></li>
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<li><a href="#basic-correlation-analysis">Basic Correlation Analysis</a>
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<li><a href="#short-background" id="toc-short-background">Short Background</a></li>
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<li><a href="#why-run-these-surveys" id="toc-why-run-these-surveys">Why Run These Surveys?</a></li>
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<li><a href="#whats-in-the-survey" id="toc-whats-in-the-survey">What’s in the Survey?</a></li>
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<li><a href="#some-interesting-examples" id="toc-some-interesting-examples">Some Interesting Examples</a></li>
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<li><a href="#basic-correlation-analysis" id="toc-basic-correlation-analysis">Basic Correlation Analysis</a>
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<ul>
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<li><a href="#correlations-sliced-by-time">Correlations Sliced by Time</a></li>
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<li><a href="#correlations-sliced-by-county">Correlations Sliced by County</a></li>
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<li><a href="#correlations-sliced-by-time" id="toc-correlations-sliced-by-time">Correlations Sliced by Time</a></li>
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<li><a href="#correlations-sliced-by-county" id="toc-correlations-sliced-by-county">Correlations Sliced by County</a></li>
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</ul></li>
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<li><a href="#whats-next-with-the-surveys">What’s Next with the Surveys</a></li>
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<li><a href="#whats-next-with-the-surveys" id="toc-whats-next-with-the-surveys">What’s Next with the Surveys</a></li>
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</ul>
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</div>
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title = &quot;Daily new confirmed COVID-19 cases per 100,000 people&quot;,
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range = c(0, 30), choro_params = list(subtitle = subtitle))
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grid.arrange(p1, p2, nrow = 1)</code></pre>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-2-1.svg" width="960" class="wide-figure" /></p>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-3-1.svg" width="960" class="wide-figure" /></p>
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<p>We generated these plots using our <a href="https://cmu-delphi.github.io/covidcast/covidcastR/">covidcast R
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package</a>.
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In all, fetching the data from our API and producing the heatmaps
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linetype = 2, size = 1, color = ggplot_colors[1]) +
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geom_vline(xintercept = as.numeric(as.Date(&quot;2020-06-25&quot;)),
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linetype = 2, size = 1, color = ggplot_colors[2])</code></pre>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-3-1.svg" width="576" /></p>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-4-1.svg" width="576" /></p>
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<p>This example, as with all code examples in this blog post, was produced using
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our <a href="https://cmu-delphi.github.io/covidcast/covidcastR/">covidcast R package</a>.</p>
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<p>A first glance reveals that the % CLI-in-community indicator
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p_list[[i]] = plot_one(geo_values[i], legend = FALSE)
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}
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do.call(grid.arrange, c(p_list, nrow = 5, ncol = 4))</code></pre>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-4-1.svg" width="960" class="wide-figure" /></p>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-5-1.svg" width="960" class="wide-figure" /></p>
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<p>The examples above are an informal way of looking
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at the <em>recall</em> of the % CLI-in-community signal.
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Of course, this is only one half of the story:
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subtitle = sprintf(&quot;Over all counties with at least %i cumulative cases&quot;,
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case_num), x = &quot;Date&quot;, y = &quot;Correlation&quot;) +
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theme_bw() + theme(legend.pos = &quot;bottom&quot;, legend.title = element_blank())</code></pre>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-5-1.svg" width="576" /></p>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-6-1.svg" width="576" /></p>
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<p>Another interesting observation is that the correlations from either indicator
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increase dramatically sometime around mid-June.
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This could be because many counties saw big surges in COVID-19 activity around
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subtitle = sprintf(&quot;Over all counties with at least %i cumulative cases&quot;,
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case_num), x = &quot;Date&quot;, y = &quot;Median abs deviation&quot;) +
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theme_bw()</code></pre>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-6-1.svg" width="576" /></p>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-7-1.svg" width="576" /></p>
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</div>
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<div id="correlations-sliced-by-county" class="section level3">
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<h3>Correlations Sliced by County</h3>
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subtitle = sprintf(&quot;Over all counties with at least %i cumulative cases&quot;,
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case_num), x = &quot;Correlation&quot;, y = &quot;Density&quot;) +
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theme_bw() + theme(legend.pos = &quot;bottom&quot;, legend.title = element_blank())</code></pre>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-7-1.svg" width="576" /></p>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-8-1.svg" width="576" /></p>
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<p>We can also examine choropleth maps of these correlations to learn
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where (geographically speaking) they’re high and where they’re not.
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As we can see from the maps below, the % CLI-in-community indicator
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p2 = plot(df_cor2, title = &quot;Correlation between % CLI-in-community and case rates&quot;,
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range = c(-1, 1), choro_col = cm.colors(10))
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grid.arrange(p1, p2, nrow = 1)</code></pre>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-8-1.svg" width="960" class="wide-figure" /></p>
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<p><img src="/blog/2020-08-26-fb-survey_files/figure-html/unnamed-chunk-9-1.svg" width="960" class="wide-figure" /></p>
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</div>
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</div>
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<div id="whats-next-with-the-surveys" class="section level2">

‎content/blog/2020-08-28-api.html

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toc: true
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---
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<script src="/rmarkdown-libs/header-attrs/header-attrs.js"></script>
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<div id="TOC">
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<ul>
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<li><a href="#purpose-of-the-api">Purpose of the API</a></li>
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<li><a href="#unique-data-sources">Unique Data Sources</a></li>
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<li><a href="#tracking-observations-and-revisions">Tracking Observations and Revisions</a></li>
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<li><a href="#accessing-the-api">Accessing the API</a></li>
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<li><a href="#putting-the-api-to-work">Putting the API to Work</a></li>
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<li><a href="#purpose-of-the-api" id="toc-purpose-of-the-api">Purpose of the API</a></li>
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<li><a href="#unique-data-sources" id="toc-unique-data-sources">Unique Data Sources</a></li>
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<li><a href="#tracking-observations-and-revisions" id="toc-tracking-observations-and-revisions">Tracking Observations and Revisions</a></li>
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<li><a href="#accessing-the-api" id="toc-accessing-the-api">Accessing the API</a></li>
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<li><a href="#putting-the-api-to-work" id="toc-putting-the-api-to-work">Putting the API to Work</a></li>
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</ul>
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</div>
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knitr::kable(&quot;html&quot;, digits = 2,
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col.names = c(&quot;Test date&quot;, &quot;Positivity rate (%)&quot;, &quot;Sample size&quot;,
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&quot;Issued on&quot;, &quot;Lag (days)&quot;))</code></pre>
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<thead>
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Test date
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Positivity rate (%)
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<p><em>November 2023 update: Quidel data is no longer publicly available, so the table generated by the code chunk above has been removed.</em></p>
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<p>Many data sources are subject to revisions:</p>
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<ul>
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<li>Case and death counts are frequently corrected or adjusted by authorities.</li>

‎content/blog/2020-09-18-google-survey.html

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<ul>
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<li><a href="#short-background">Short Background</a></li>
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<li><a href="#cli-in-community">CLI-in-Community</a></li>
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<li><a href="#our-two-surveys">Our Two Surveys</a></li>
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<li><a href="#google-survey-redux">Google Survey Redux</a></li>
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<li><a href="#short-background" id="toc-short-background">Short Background</a></li>
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<li><a href="#cli-in-community" id="toc-cli-in-community">CLI-in-Community</a></li>
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<li><a href="#our-two-surveys" id="toc-our-two-surveys">Our Two Surveys</a></li>
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<li><a href="#google-survey-redux" id="toc-google-survey-redux">Google Survey Redux</a></li>
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</div>
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<p>In both plots, we see a reassuring trend,
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but the trend on the left is noticeably stronger.
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Indeed, the correlation here between the Google signal and case rates is
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To be fair, we should note that the Google signal comprises a much
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or manually (in collaboration with partners in public health and/or data
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journalists). Stay tuned to the Delphi blog for updates.</p>
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<div class="footnotes footnotes-end-of-document">
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<hr />
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<ol>
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<li id="fn1"><p>In the survey methodology literature, a “proxy question” is one in which

‎content/blog/2020-10-14-dv-signal.html

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<li><a href="#motivation">Motivation</a></li>
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<li><a href="#the-doctor-visits-indicator">The Doctor Visits Indicator</a></li>
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<li><a href="#backfill">Backfill</a></li>
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<li><a href="#weekday-effects">Weekday Effects</a></li>
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<li><a href="#basic-correlation-analyses">Basic Correlation Analyses</a></li>
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<li><a href="#spatial-heterogeneity">Spatial Heterogeneity</a></li>
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<li><a href="#exciting-new-data">Exciting New Data</a></li>
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<li><a href="#motivation" id="toc-motivation">Motivation</a></li>
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<li><a href="#the-doctor-visits-indicator" id="toc-the-doctor-visits-indicator">The Doctor Visits Indicator</a></li>
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<li><a href="#backfill" id="toc-backfill">Backfill</a></li>
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<li><a href="#weekday-effects" id="toc-weekday-effects">Weekday Effects</a></li>
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<li><a href="#basic-correlation-analyses" id="toc-basic-correlation-analyses">Basic Correlation Analyses</a></li>
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<li><a href="#spatial-heterogeneity" id="toc-spatial-heterogeneity">Spatial Heterogeneity</a></li>
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<li><a href="#exciting-new-data" id="toc-exciting-new-data">Exciting New Data</a></li>
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